Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans.
The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a “gold standard” was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution.
Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard.
The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.
The authors wish to thank the Gilbert Israeli Neurofibromatosis Center (GINFC) and the Lucile Packard Children's Hospital at Stanford (LPCH), for their contribution with providing the real data and supporting the medical part of the paper. The authors are also heartily thankful to Vicki Myers for editorial assistance. This research was partially supported by Kamin Grant No. 46217 “Computer-based tumors analysis and follow-up in radiological oncology studies” from the Israeli Ministry of Trade and Industry, and partially supported by a grant from the National Cancer Institute, National Institutes of Health, U01CA142555, “Computerized Quantitative Imaging Assessment of Tumor Burden.”
II.A. Step 1: Coregistration of MRI sequences
II.B. Step 2: Detection of changing tumor boundaries
II.C. Step 3: Tumor internal classification
II.C.1. Modeling tumor components gray levels distribution
II.C.2. Modeling tumor components temporal transitions
II.C.3. Model parameters estimation
II.D. Step 4: Tumor boundaries update
II.E. Data acquisition
III. EXPERIMENTAL RESULTS
III.A. Data evaluation
III.B. Training procedure
III.C. Validation results
III.C.1. Gross total volume results
III.C.2. Tumor components longitudinal tracking results
III.D. Parameter sensitivity analysis
IV.A. Comparison with other tumor segmentation methods
IV.B. Comparison with general segmentation algorithms
IV.C. Relation to prior research
IV.D. Baseline segmentation
IV.E. Error propagation
IV.F. Tumor regression
V.A. Follow-up of tumor components
V.B. No need for gray-level normalization
V.C. Clinical significance
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